Neighbor selection for variance reduction in I/sub DDQ/ and other parametric data

The subject of this paper is variance reduction and nearest neighbor residual estimates for I/sub DDQ/ and other continuous-valued test measurements. The key, new concept introduced is data-driven neighborhood identification about a die to reduce the variance of good and faulty I/sub DDQ/ distributions. Using LSI Logic production data, neighborhood selection techniques are demonstrated. The main contribution of the paper is variance reduction by the systematic use of the die location and wafer- or lot-level patterns and improved identification of die outliers of continuous-valued test data such as I/sub DDQ/.